Python Tutorial
Pandas DataFrames
A DataFrame is a table: named columns, an index, and rows of values. Most Pandas work happens here.
From a Dictionary
import pandas as pd
data = {
"name": ["Luna", "Kai", "Mia"],
"score": [88, 92, 95],
"passed": [True, True, True],
}
df = pd.DataFrame(data)
print(df)Named Index
df = pd.DataFrame(data, index=["a", "b", "c"])
print(df.loc["a"])One Column Is a Series
print(df["score"])
print(type(df["score"])) # <class 'pandas.Series'>📘 Real-World Deep Dive
Knowing <strong>Pandas Dataframes (pandas)</strong> well is what turns pandas from a curiosity into a daily tool — you'll reach for it in nearly every real project.
Real-Life Scenario
An end-to-end usage of Pandas Dataframes that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import pandas as pd
df = pd.DataFrame({
"id": [1, 2, 3],
"amount": [10.0, 20.5, 7.25],
})
print(df.head())
print(df["amount"].mean())Expected Output
(see source)Common mistakes
- A
DataFrameindexing pattern likedf[df.col > 5]returns a copy — use.loc[row_mask, col]for assignment to avoidSettingWithCopyWarning. - Pandas infers
objectdtype for CSVs with mixed numeric/text columns; cast withpd.to_numeric/astype("category")for big speed/memory wins. df.iterrows()is O(n) and slow; iterate withdf.itertuples()or vectorise column-wise.- Treating Pandas Dataframes as a black box without reading the docs — the API has subtle defaults that bite when you scale.
🚀 Performance & Best Practices
- Enable the Arrow backend:
pd.read_csv("…", engine="pyarrow", dtype_backend="pyarrow")for faster, type-stable reads. - Use
categoricaldtype for columns with low-cardinality strings — sort/join/group-by speed up dramatically. - Switching a hot loop from row-wise Python to
df.eval("…")/df.query("…")often gives 5–50×. - When working with pandas, prefer vectorised / batched operations over Python loops.
🧪 Try It Yourself
- Reproduce the snippet on a representative slice of your own data.
- Profile the snippet with
cProfileortimeitand find the single biggest improvement. - Generalise the snippet into a small, reusable function you can drop into future projects.